Almost complete KDB

src/BaseClassifier.cc

@@ -12,7 +12,6 @@ namespace bayesnet {
         this->features = features;
         this->className = className;
         this->states = states;
-        cout << "Checking fit parameters" << endl;
         checkFitParameters();
         train();
         return *this;

src/KDB.cc

@@ -4,6 +4,14 @@
 namespace bayesnet {
     using namespace std;
     using namespace torch;
+    vector<int> argsort(vector<float>& nums)
+    {
+        int n = nums.size();
+        vector<int> indices(n);
+        iota(indices.begin(), indices.end(), 0);
+        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
+        return indices;
+    }
     KDB::KDB(int k) : BaseClassifier(Network()), k(k) {}
     void KDB::train()
     {
@@ -31,14 +39,45 @@ namespace bayesnet {
         cout << "Computing mutual information between features and class" << endl;
         auto n_classes = states[className].size();
         auto metrics = Metrics(dataset, features, className, n_classes);
+        vector <float> mi;
         for (auto i = 0; i < features.size(); i++) {
             Tensor firstFeature = X.index({ "...", i });
-            Tensor secondFeature = y;
+            mi.push_back(metrics.mutualInformation(firstFeature, y));
-            double mi = metrics.mutualInformation(firstFeature, y);
+            cout << "Mutual information between " << features[i] << " and " << className << " is " << mi[i] << endl;
-            cout << "Mutual information between " << features[i] << " and " << className << " is " << mi << endl;
+        }
-
+        // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
+        auto conditionalEdgeWeights = metrics.conditionalEdge();
+        cout << "Conditional edge weights" << endl;
+        cout << conditionalEdgeWeights << endl;
+        // 3. Let the used variable list, S, be empty.
+        vector<int> S;
+        // 4. Let the DAG network being constructed, BN, begin with a single
+        // class node, C.
+        model.addNode(className, states[className].size());
+        cout << "Adding node " << className << " to the network" << endl;
+        // 5. Repeat until S includes all domain features
+        // 5.1. Select feature Xmax which is not in S and has the largest value
+        // I(Xmax;C).
+        auto order = argsort(mi);
+        for (auto idx : order) {
+            cout << idx << " " << mi[idx] << endl;
+            // 5.2. Add a node to BN representing Xmax.
+            model.addNode(features[idx], states[features[idx]].size());
+            // 5.3. Add an arc from C to Xmax in BN.
+            model.addEdge(className, features[idx]);
+            // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
+            // the highest value for I(Xmax;X,jC).
+            // auto conditionalEdgeWeightsAccessor = conditionalEdgeWeights.accessor<float, 2>();
+            // auto conditionalEdgeWeightsSorted = conditionalEdgeWeightsAccessor[idx].sort();
+            // auto conditionalEdgeWeightsSortedAccessor = conditionalEdgeWeightsSorted.accessor<float, 1>();
+            // for (auto i = 0; i < k; ++i) {
+            //     auto index = conditionalEdgeWeightsSortedAccessor[i].item<int>();
+            //     model.addEdge(features[idx], features[index]);
+            // }
+            // 5.5. Add Xmax to S.
+            S.push_back(idx);
         }
 
-
     }
+
 }

src/Metrics.cc

@@ -30,7 +30,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<float> Metrics::conditionalEdgeWeights()
+    torch::Tensor Metrics::conditionalEdge()
     {
         auto result = vector<double>();
         auto source = vector<string>(features);
@@ -65,6 +65,11 @@ namespace bayesnet {
             matrix[x][y] = result[i];
             matrix[y][x] = result[i];
         }
+        return matrix;
+    }
+    vector<float> Metrics::conditionalEdgeWeights()
+    {
+        auto matrix = conditionalEdge();
         std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
         return v;
     }

src/Metrics.hpp

@@ -19,6 +19,7 @@ namespace bayesnet {
         Metrics(torch::Tensor&, vector<string>&, string&, int);
         Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
         vector<float> conditionalEdgeWeights();
+        torch::Tensor conditionalEdge();
     };
 }
 #endif